Optimistic Concurrency Control for Distributed Unsupervised Learning

Authors

Conference Event Type: Poster

Abstract

Research on distributed machine learning algorithms has focused primarily on one of two extremes---algorithms that obey strict concurrency constraints or algorithms that obey few or no such constraints. We consider an intermediate alternative in which algorithms optimistically assume that conflicts are unlikely and if conflicts do arise a conflict-resolution protocol is invoked. We view this optimistic concurrency control'' paradigm as particularly appropriate for large-scale machine learning algorithms, particularly in the unsupervised setting. We demonstrate our approach in three problem areas: clustering, feature learning and online facility location. We evaluate our methods via large-scale experiments in a cluster computing environment. "

Neural Information Processing Systems (NIPS)

This is the β (beta) version of our new papers site, where you can find any paper published at the Neural Information Processing Systems Conference.